Stephen Wolfram

Maybe this is one of these things where I should try and get an LLM to help me.

How to make it speakable.

Maybe it's easier than you realize.

I think it is easier.

I think it's one idea or so.

I think it's going to be something where, you know, the fact is it's a tree-structured language, just like human language is a tree-structured language.

And I think it's going to be one of these things where one of the requirements that I've had is that whatever the spoken version is,

that dictation should be easy.

That is, that shouldn't be the case that you have to relearn how the whole thing works.

It should be the case that, you know, that open bracket is just a ah or something.

And it's, you know, and then, but, you know, human language has a lot of tricks that are, I mean, for example, human language,

has features that are sort of optimized, keep things within the bounds that our brains can easily deal with.

Like I tried to teach a transformer neural net to do parenthesis matching.

It's pretty crummy at that.

And then ChatGPT is similarly quite crummy at parenthesis matching.

You can do it for small parenthesis things, for the same size of parenthesis things where if I look at it as a human, I can immediately say these are matched, these are not matched.

But as soon as it gets big, as soon as it gets kind of to the point where sort of a deeper computation, it's hopeless.

But the fact is that human language has avoided, for example, the deep sub-clauses.

We arrange things so we don't end up with these incredibly deep things because brains are not well set up to deal with that.

And it's found lots of tricks.